Method of evaluation wind flow based on conservation of momentum and variation in terrain

ABSTRACT

A method of modeling the spatial variation in wind resource at a prospective wind farm site. The method involves a simplified analysis of the Navier-Stokes equation and utilizes data from all of the met sites simultaneously to develop site-calibrated models. The model coefficients, m UW  and m DW , describe the sensitivity of the wind speed to changes in the upwind and downwind terrain exposure and are defined for downhill and uphill flow. The coefficients are a function of terrain complexity and, since terrain complexity can change across an area, the estimates are performed in a stepwise fashion where a path of nodes with a gradual change in complexity is found between each pair of sites. Also, coefficients are defined for each wind direction sector and estimates are performed on a sectorwise basis. The site-calibrated models are created by cross-predicting between each pair of met sites and, through a self-learning technique, the model coefficients that yield the minimum met cross-prediction error are found.

FIELD OF THE INVENTION

The present invention relates to a method to estimate and predict thespatial variation in wind energy resource at a prospective wind energysite. In particular, the invention utilizes the theory of conservationof momentum (Navier-Stokes) and wind data measured at two or moremeteorological (met) towers to develop a wind flow model based on howthe upwind and downwind terrain changes between the respective mettowers and on whether the wind flow is uphill, downhill, or over a hill.

BACKGROUND OF THE INVENTION

In wind energy resource assessment, it is the primary goal to estimatethe annual net energy that could be produced from a potential wind farm.This assessment includes several elements such as a wake loss model,long-term climatic adjustments, and a wind flow model. The wind flowmodel is the foundation of the wind resource assessment as it is used toestimate the free-stream (un-waked) wind speed distribution across theproject area, which is then converted into gross annual energyproduction. If the wind flow model is flawed or biased, then allsubsequent calculations will inherit those errors and the assessmentwill not be representative of the wind farm's true potential.

A wind flow model is developed by first taking measurements of the windspeed and direction typically at one or more meteorological tower (whichcould include a physical tower, or could include a remote sensing devicesuch as a LIDAR or SODAR device) sites within the project boundaries.Characteristic wind measurements are collected with anemometers and windvanes mounted typically at several levels on a tower or mast, called ameteorological (met) tower, or may be collected by a remote sensingdevice such as a SODAR or LIDAR. As used herein, a meteorological (met)tower is defined to mean any measurement of meteorologicalcharacteristics, whether from sensors mounted at one or more heights ona physical tower or from a remote sensing device such as a SODAR or aLIDAR. Prospective wind farm projects often have multiple met towers,although, in some cases, only one tower may be present within theproject area. Often, the period of record for on-site measurements isnot representative of long-term climatic conditions. To adjust on-sitemeasurements to long-term conditions, project meteorological data iscorrelated to a long-term reference data set. On-site data is adjustedbased on those correlations to reflect long-term climatic conditions.Then a joint frequency distribution of measured wind speed and winddirection is developed for each met tower. The joint frequencydistribution is normalized for an average year. One of ordinary skill inthe art can develop an accurate representation of climatology at aprospective wind farm meteorological tower site, thus the particularsare not discussed here. Further detail of the measurement andcalculation of wind climatology can be found in Wind Characteristics, byJanardan Rohatgi and published by the Alternative Energy Institute, WestTexas A&M University, 1994, incorporated herein by reference.

Measurements from met towers each represent one point in a project area,and not necessarily where wind turbines will be placed. To account forand predict the wind energy resource across a site, a wind flow model isused. Once characteristic representations of the wind climatology aredeveloped for a particular met tower, the joint frequency distributionsare used in a wind flow model to extrapolate and predict the wind energyresource spatially across the project area.

Wind flow across a given area is not typically consistent from point topoint. On-site measurements typically show that there is spatialvariation in wind speed and direction. Many aspects affect the variationin the wind regime, including trees, shrubs, buildings, and othersurface “roughness” elements. Another aspect that affects the windregime of a given site is the variation in terrain elevation, which isknown as “terrain effects.” Analysis of wind regimes in areas withcomplex terrain with large differences in elevation across the site hasdemonstrated significant differences in the representative frequencydistributions at different met towers, indicating that the terraineffects have a large influence upon the local wind climatology. Inflatter sites, while the variation in measurements between met towers issmaller, it has been shown that terrain effects are still significant inaffecting the spatial wind flow.

There are several different types of commercially-available wind flowmodels. All use a derived joint frequency distribution representing theclimatology at the site of a met tower, elevation data, and otherinputs, which can include surface roughness values and forest canopyheights. The wind flow models that are currently most commonly usedinclude linear models and computational fluid dynamics (CFD) models. Ingeneral, linear models are viewed as simple and quick to produceestimates but are known to produce estimates with significant error,particularly in complex terrain. One of the most widely used linearmodels is the Wind Atlas Analysis and Application Program (WAsP), whichwas developed by Risø DTU, Denmark. WAsP has been documented to havesignificant error when used to predict the wind flow in sites withslopes more than 20 degrees, as detailed in WAsP Prediction Errors Dueto Site Orography, by Anthony J. Bowen and Niels G. Mortensen, publishedby Risø National Laboratory, 2004, incorporated herein by reference.

On the other end of the spectrum, CFD models are considered to be morerobust and can produce estimates with lower error. The most well-knownCFD software models are Meteodyn and WindSim. CFD models are typicallyvery complex and require extensive training, resources, and knowledge touse to accurately predict wind flow across a site.

Several validation studies have been conducted where linear and CFDmodels have been compared. In general, there have not been consistentresults showing the superiority of either linear or CFD models. Someresults that show the WAsP linear model performing as well as or betterthan the CFD models. However, some studies showed the CFD modelproducing a lower error than WAsP. In general, it is expected that CFDmodels should produce a more accurate wind flow model. However, this isnot always the case, and the error in CFD models can be substantial.

SUMMARY OF THE INVENTION

The present invention can be viewed as the middle ground between linearand CFD models. Similar to previous wind flow models, the presentinvention uses the derived wind frequency distribution for ameteorological (met) tower and digital elevation data. The majority ofprevious wind flow models only have the capability of using a singlewind frequency distribution whereas the present invention is capable ofusing wind frequency distributions from multiple met towers at the sametime.

The present invention utilizes all available meteorological tower sitessimultaneously to generate site-specific wind flow models that describethe difference in the wind speed from a met site to a target site (forexample, a proposed wind turbine location, a grid node if the model isused to create a grid of wind speed estimates, or any other location ofinterest for which it is desired to estimate the annual average windspeed) based on the difference in the upwind and downwind terrainexposure between the two sites. Exposure is a mathematicalrepresentation of elevation differences between the point in question,typically a met tower site or prospective wind turbine site, and thesurrounding terrain out to a specified radius. Exposure is disclosed inU.S. Pat. No. 8,483,963, entitled METHOD OF EVALUATING WIND FLOW BASEDON TERRAIN EXPOSURE AND ELEVATION, John Bertrand Kline, incorporatedherein by reference.

The invention describes the sensitivity of the wind speed to changes inthe upwind (UW) and downwind (DW) terrain exposure by using twocoefficients, m_(UW) and m_(DW). The UW coefficient, m_(UW), representsthe sensitivity of the wind speed to changes in the UW exposure whilethe DW coefficient, m_(DW), describes how the wind speed changes as theDW exposure varies. The physics underlying the invention tie back to thetheory of conservation of momentum of Navier-Stokes, of which moreinformation can be found in Fluid Mechanics with EngineeringApplications, 9^(th) edition, by Joseph B. Franzini and John E.Finnermore, incorporated herein by reference. By making simplifyingassumptions regarding the uniformity of the wind conditions, theinvention estimates the wind speed from one met site to another basedsolely on how the terrain changes between the sites. This innovationdoes not consider surface roughness or other elements, solely theinfluence of terrain effect upon wind flow.

Three sets of model coefficients are defined in the invention thatrepresent downhill flow, uphill flow, and induced speed-up over hills.The coefficients are dependent on the level of terrain complexity.Log-log relationships are used to describe the coefficients as afunction of terrain complexity. Also, model coefficients are defined foreach wind direction sector.

When forming the site-calibrated model, first a default set of modelcoefficients are used to cross-predict the met tower site wind speeds,and the overall cross-prediction error is determined. Then, using aself-learning algorithm, the coefficient relationships aresystematically altered and the relationships that yield the minimum metcross-prediction root mean square (RMS) error are found, which definethe site-calibrated model.

The terrain exposure is calculated using four different radii ofinvestigation (for example: 4000, 6000, 8000 and 10,000 m) and asite-calibrated model is formed using the exposures calculated for eachradius. These models are then used to form estimates of the wind speedand gross annual energy production (AEP) at the turbine sites and/or atmap nodes.

Since the model coefficients vary as a function of terrain complexity,the predictions between sites are conducted in a stepwise fashion wherethe wind speed is estimated along a path of nodes that have a gradualchange in terrain complexity from one met site to another. Between eachpair of met sites and from each met site to every turbine site or mapnode, a path of nodes is created where there is a small change in theexposure and elevation from one node to the next. For each step alongthe path of nodes, the sectorwise UW and DW model coefficients aredetermined from the site-calibrated relationships based on the terraincomplexity and whether the flow is downhill or uphill. The change inwind speed is then estimated along the path of nodes as the UW and DWexposure changes from the met site to the turbine or map node.

Wind speed and gross energy estimates are formed at the turbine or mapnode using each met site and each site-calibrated model. Then, based onthe similarity of the UW and DW exposure between the predictor met andthe target site, wind speed weights are assigned to each estimate.Additionally, the RMS of the met cross-prediction error is found foreach site-calibrated model and the RMS error is used as a weight. Thefinal estimate at the turbine or map node is therefore a weightedaverage of all estimates formed from each met site weighted by theterrain similarity and the RMS error of the met cross-prediction of eachsite-calibrated model.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages according to embodiments of the invention willbe apparent from the following Detailed Description taken in conjunctionwith the accompanying drawings, in which:

FIG. 1 shows a site with downhill flow with a positive DW exposure.

FIG. 2 shows a site with downhill flow with a negative UW exposure.

FIG. 3 shows a site with an uphill flow where there is a negative DWexposure.

FIG. 4 shows a site with an uphill flow where there is a positive UWexposure (when UW exposure>UW critical).

FIG. 5 shows a site where the UW exposure is less than UW critical.

FIG. 6 shows a graphical representation of the log-log relationships forFIGS. 1, 2, 3, 4, and 5.

FIG. 7 shows downhill model coefficients by wind direction for a terraincomplexity defined by the P10 exposure at 30 m for radii ofinvestigation of 4000 m, 6000 m, 8000 m, and 10000 m.

FIG. 8 shows speed-up for when the UW exposure is less than UW criticalby wind direction for a terrain complexity defined by the P10 exposureat 30 m for radii of investigation of 4000 m, 6000 m, 8000 m, and 10000m.

FIG. 9 shows downhill model coefficients by wind direction for a terraincomplexity defined by the P10 exposure at 30 m for radii ofinvestigation of 4000 m, 6000 m, 8000 m, and 10000 m.

FIG. 10 shows the downhill model coefficients by terrain complexity (P10exposure) for the sector of wind from 240 degrees to 300 degrees at aradius of investigation of 6000 m.

FIG. 11 shows the uphill model coefficients by terrain complexity (P10exposure) for the sector of wind from 240 degrees to 300 degrees at aradius of investigation of 6000 m.

FIG. 12 shows the speed-up model coefficients by terrain complexity (P10exposure) for the sector of wind from 240 degrees to 300 degrees at aradius of investigation of 6000 m.

FIG. 13 shows actual wind speeds vs. estimated wind speeds at the metsites for the example.

FIG. 14 shows a flow chart of the steps to use the invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention disclosed herein describes a new model to predict thespatial variation of wind flow at a site. The invention uses derivationsof Newton's second law, which states that the change in momentum of amoving fluid is equal to the net force acting on that fluid. TheNavier-Stokes equation is used to represent Newton's second law, and isdescribed on pages 190-191 of Fluid Mechanics with EngineeringApplications, 9th Edition, 1997 by Joseph B. Franzini and John E.Finnermore, and incorporated herein by reference. Further detail on thederivations of the equations behind the present invention fromNavier-Stokes can be found in Continuum Wind Flow Model: Introduction toModel Theory and Case Study Review by Elizabeth Walls, incorporatedherein by reference. In the following discussion, the followingvariables are defined:

UW: Upwind

DW: Downwind

θ: Slope of terrain.

WS: Wind speed.

WD: Wind direction sector

X_(i)=the i^(th) easting coordinate in the digital elevation model(DEM).

Y_(i)=the i^(th) northing coordinate in the DEM.

Z_(i)=the elevation in the DEM.

X₀=the easting of the site in question.

Y₀=the northing of the site in question.

Z₀=the elevation of the site in question.

R_(i)=the distance from the site to the i^(th) coordinate in the DEM.

As referenced in Continuum Wind Flow Model: Introduction to Model Theoryand Case Study Review by Elizabeth Walls and incorporated herein byreference, in the Navier-Stokes conservation of momentum equation, theeffect of changing the UW and DW terrain on the acceleration andtherefore the velocity of the wind is a function of the UW and DWterrain slope, θ. In the present innovation, instead of calculating theaverage terrain slope, an equivalent measurement (terrain exposure) isused. In addition, in the present invention, the vertical pressuregradient force, gravity and velocity flow field are replaced by thecoefficients, m_(DW) and m_(UW). This assumes that, for a given winddirection sector and for a given level of terrain complexity, thevertical pressure gradient force, the force of gravity, and the windspeed flow field are constant from one site to another within theproject area.

Terrain exposure (or, simply, exposure) is the weighted averageelevation difference, Z, between a site and the surrounding terrainwithin a specified radius of investigation, which is disclosed in U.S.Pat. No. 8,483,963, entitled METHOD OF EVALUATING WIND FLOW BASED ONTERRAIN EXPOSURE AND ELEVATION, John Bertrand Kline, incorporated hereinby reference.

${Exposure} = {\left( {\sum_{i = 0}^{N}\frac{Z_{o} - Z_{i}}{d_{z_{o} - z_{i}}}} \right)\text{/}\left( {\sum_{i = 0}^{N}\frac{1}{d_{z_{o} - z_{i}}}} \right)}$

In the simplified Navier-Stokes analysis, the term, sin θ, is theequivalent of the quotient of exposure and the radius of investigation:

${{\sin \; \theta} \equiv \frac{\overset{\_}{Z}}{R}} = \frac{Exposure}{R}$

The equations below show the results of the simplified Navier-Stokesanalysis where the flow is downhill DW of the site and is uphill UW ofthe site. If the wind conditions are approximately uniform for a givenwind direction sector and for a certain level of terrain complexity thenthe vertical pressure gradient, P_(z)/ρ, the force of gravity, g, thewind speed flow field,

$\left( {\frac{\partial v_{x}}{\partial x} + \frac{\partial v_{z}}{z}} \right).$

and the radius of investigation, R, can all be condensed down to berepresented by the linear coefficients, m_(DW) and m_(UW).

${\Delta \; {WS}} = {{\frac{\left( {g - \frac{P_{z}}{\rho}} \right)}{\left( {\frac{\partial v_{x}}{\partial x} + \frac{\partial v_{z}}{\partial z}} \right)_{DW}}*\frac{\Delta \; {DW}}{R}} = {m_{DW}*\Delta \; {DW}}}$${\Delta \; {WS}} = {{\frac{\left( {\frac{P_{z}}{\rho} - g} \right)}{\left( {\frac{\partial v_{x}}{\partial x} + \frac{\partial v_{z}}{\partial z}} \right)_{UW}}*\frac{\Delta \; {UW}}{R}} = {m_{UW}*\Delta \; {UW}}}$

The net change in wind speed from one site to another can then beestimated by calculating the differences in the UW and DW exposure andmultiplying the changes in the UW and DW exposure by the respectivecoefficients, m_(DW) and m_(UW), as shown below:

ΔWS=m _(UW)*ΔUW+m _(DW)*ΔDW

Since the model coefficients, m_(UW) and m_(DW), are a function of theterrain complexity, a quantitative representation of the terraincomplexity is defined. The terrain complexity is found by creating agrid around the site (i.e. met, turbine, node, or map node) and thencalculating the exposure at each node within the gridded area. Thecalculated exposures are then sorted and the exposure in the toppredetermined percentile is deemed the measure of the level of terraincomplexity directly surrounding the site. In general, using a P10exposure has been found to work well as a terrain complexity metric, butother values could be used; for example a P5 or P20 exposure, whichwould represent the top 5% or top 20% percentiles, respectively. Theidea behind terrain complexity is that one can represent and quantifythe complexity of the surrounding terrain based on how high the exposureis within the surrounding terrain. Other metrics to quantify terraincomplexity may also be used, and method of quantifying terrain exposuredescribed herein is presented by way of example as one possible metricthat has been found to work well. One other possible metric to quantifyterrain complexity could include the Ruggedness Index (RIX) as used inthe WAsP model. Other possible metrics will be evident to one ofordinary skill in the art.

The model coefficients are defined as a function of terrain complexityin log-log relationships. There are three log-log relationships definedto represent sites with positive or negative UW and DW exposures and fordownhill flow, uphill flow where the wind decelerates or inducedspeed-up over hills. In Scenario 1, the log-log relationship representssites with downhill flow with a positive DW exposure as shown in FIG. 1,or negative UW exposure, as shown in FIG. 2. In Scenario 2, the site hasan uphill flow, where there is a negative DW exposure as shown in FIG.3, or a positive UW exposure (when UW exposure>UW critical) as shown inFIG. 4. In Scenario 3, a relationship is used to define the inducedspeed-up caused by a hill where the UW exposure is less than UWcritical, as shown in FIG. 5. In FIGS. 1 through 5, 1 is a site ofinterest (typically a met tower or turbine location), 2 is the directionof the wind, 3 is the upwind exposure and 4 is the downwind exposure.FIG. 6 shows a graphical representation of the log-log relationships foreach scenario.

Since the terrain complexity can change across a project area, the modelcoefficient, which describes the change in wind speed with variations inthe exposure, should also vary across the project area. To account forthis variability, the present invention creates a path of nodes thathave a gradual change in terrain complexity and elevation between twosites (met towers, turbine locations, or other points) when estimatingthe wind speeds from one site to another. The algorithm selects the paththat has the gentlest slope between the two sites and selects nodeslocated on high points (as opposed to in a valley, for example).

Once a path between the predictor and target site has been found, thewind speed is calculated along the path of nodes and an estimate at thetarget site is formed. For each node, the UW and DW coefficients aredetermined from the three log-log relationships, and are based on theterrain complexity and whether the UW and DW exposures are positive ornegative.

One of the assumptions in the present innovation is that the meanatmospheric stability, surface roughness and density are constant fromone site to another and that only changes in the terrain will alter thewind speed. Since, at some sites, the mean atmospheric stability can bequite different as a function of wind direction, the wind speed isestimated from one site to another on a sectorwise basis where adifferent set of log-log relationships are defined for each winddirection sector. This allows the model coefficients to be a function ofnot only terrain complexity but also of mean atmospheric stability.

WS_(i,j+1)=WS_(i,j) +m _(UWi,j)*(UW_(i,j+1)−UW_(i,j))+m_(DWi,j)*(DW_(i,j+1)−DW_(i,j))

where i=1 to number of wind direction sectors

where j=1 to number of nodes in path

Once the sectorwise wind speed estimates have been formed at the targetsite, they are combined by multiplying with the wind rose as measured atthe predictor met site to form the overall wind speed estimate:

${WS} = {\sum\limits_{i = 1}^{WD}\; {{WS}_{i,N}*{Wind}\mspace{14mu} {Rose}\mspace{14mu} {Frequency}_{i}}}$

For each wind direction sector, three log-log relationships define themodel coefficients as a function of the terrain complexity. Since theterrain complexity can change across a project area, the presentinnovation uses a stepwise approach by creating a path of nodes with agradual change in complexity and the wind speed is estimated from themet site along the path of nodes to the target site. This is done bothwhen conducting the met cross-predictions and when generating the windspeed estimates at the turbine and/or map node sites.

To find the site-calibrated models, the present invention starts withdefault log-log relationships that have been established after analyzingdata from dozens of project sites and the wind speed between each pairof met sites is cross-predicted. The exposure is calculated using fourdifferent radii of investigation and thus four site-calibrated modelsare created. Then, through a self-learning algorithm, the log-logrelationships are systematically altered, both in terms of slope andmagnitude, and the set of log-log relationships that generate the lowestmet cross-prediction RMS error are found. These site-calibrated modelsare then used to form the wind speed estimates at the turbine sites andmap nodes. The met cross-prediction RMS error is used to estimate theuncertainty of the wind speed estimates.

Once the site-calibrated models have been determined, each met site isused individually to estimate the wind speed at turbine sites or mapnodes. A path of nodes with a gradual change in terrain complexity isformed between each met site and the target site and, with foursite-calibrated models, four wind speed estimates are formed at thetarget site using each met site as the predictor thus resulting in atotal number of wind speed estimates equal to four (number ofsite-calibrated models) times the number of met sites.

Often, the terrain at the target site will be more similar, in terms ofterrain complexity, to the terrain at certain met sites and weights areassigned to the wind speed estimates to reflect the relative level ofterrain similarity between the sites. The UW and DW terrain complexity(TC) is compared between each of the predicting met sites and the targetsite and a terrain weighting factor for each wind speed estimate, i, iscalculated as:

${{Terrain}\mspace{14mu} {Weight}_{i}} = {1 - \left\lbrack \frac{\left| {\Delta \; {TC}\mspace{14mu} {DW}} \middle| {}_{i}{+ \left| {\Delta \; {TC}\mspace{14mu} W} \right|_{i}} \right.}{\sum_{n = i}^{N}\left| {\Delta \; {TC}\mspace{14mu} {DW}} \middle| {}_{n}{+ \left| {\Delta \; {TC}\mspace{14mu} {UW}} \right|_{n}} \right.} \right\rbrack}$

Also, weights are calculated to reflect the accuracy of the metcross-prediction for each of the site-calibrated models. The RMS weightis calculated as shown below where the model with the lowest RMS erroris assigned a weight of 1 and the model with the highest RMS is given aweight of 0.25.

${{RMS}\mspace{14mu} {Weight}_{i}} = {1 - \frac{0.75*\left( {{RMS}_{i} - {{Min}\mspace{14mu} {RMS}_{i}}} \right)}{\left( {{{Max}\mspace{14mu} {RMS}} - {{Min}\mspace{14mu} {RMS}}} \right)}}$

Using the terrain and RMS weighting factors, the wind speed estimatesare combined using a weighted average to form the overall wind speedestimate at the target site:

Wind  Speed  Weight_(i) = RMS  Weight_(i) * Terrain  Weight_(i)${{Average}\mspace{14mu} {Wind}\mspace{14mu} {Speed}\mspace{14mu} {Estimate}} = \frac{\sum_{i = 1}^{{Num}\mspace{14mu} {{Ests}.}}{{WS}\mspace{14mu} {Weight}_{i}*{WS}\mspace{14mu} {Estimate}_{i}}}{\sum_{i = 1}^{{Num}\mspace{14mu} {{Ests}.}}{{WS}\mspace{14mu} {Weight}_{i}}}$

To illustrate the present innovation, the following example will beused. The example has complex terrain, with 11 met sites situated acrossthe project area. The 10-minute wind speeds at the top level of each metsite were extrapolated to a hub height of 80 m. These extrapolated datasets were used to form the wind speed and wind direction distributionsat each site.

The wind speed and wind direction distributions measured at each metsite are entered into the present innovation along with 30-m resolutiondigital elevation data. Four site-calibrated models are formed usingradii of investigation of 4000 m, 6000 m, 8000 m, and 10,000 m in theexposure calculation. Other radii of investigation could be used (or asingle radius of investigation could be used) but these radii are usedherein by way of example. For each model, paths of nodes with graduallychanging terrain complexity are found between each pair of met sites andthe wind speeds are cross-predicted using the default modelcoefficients. Then, through a self-learning algorithm, the downhill anduphill model coefficients are systematically altered until the metcross-prediction error reached a minimum value.

FIGS. 7, 8, and 9 show the site-calibrated model coefficients as afunction of wind direction (at a fixed terrain complexity of P10Exposure at 30 m) for the four models. As shown in FIG. 7, thewest-southwest wind direction sector (240°) yielded the largest downhillcoefficient, which was approximately 0.04 (for R=6000 m). The uphillcoefficients found through the site-calibration process were lower inmagnitude than their downhill counterparts. In general, as shown in FIG.8, the uphill coefficients were in the range of 0.01 to 0.02. FIG. 9shows the uphill coefficients when the UW exposure is less than thecritical UW exposure and there is induced speed-up over a hill. Themagnitude of these coefficients is approximately zero for the winddirection sectors of 270° to 0°, which indicates that there is noinduced speed-up due to a hill in these direction sectors. While for thewind direction sectors of 30° to 240°, the induced speed-up coefficientsare quite consistent at a magnitude of approximately 4e-5.

The model coefficients for wind direction sectors 240°, 270° and 300°are presented as a function of terrain complexity, here defined as P10exposure, in FIGS. 10, 11, and 12 for the model that used a radius of6000 m in the exposure calculation. The site-calibrated modelcoefficients all showed a dependency on terrain complexity where thecoefficients decreased in magnitude as the P10 exposure increased.

Table 1 shows the RMS errors of the met cross-prediction errors for eachof the four site-calibrated models. The lowest cross-prediction errorwas achieved by using a radius of investigation of 6000 m and the RMSerror of the met cross-predictions was 1.28%.

TABLE 1 RMS of Met Cross-Prediction Error and Model Weights Radius, mRMSE Weight 4000 2.00% 0.26 6000 1.28% 1 8000 1.55% 0.72 10000 2.01%0.25 Wgt Avg 1.53% RMS

In the present innovation, wind speed estimates are generated using eachof the four models and the estimates are weighted based on the RMS errorof the met cross-prediction. The model that used a radius of 6000 m hasthe highest weight of 1.0 while the model with the lowest weight of 0.25used a radius of 10,000 m in the exposure calculation.

Using the site-calibrated models and each met site as the predictor, thewind speed was estimated at each met site location and the estimatedwind speed ratio was compared to the actual values. FIG. 13 shows thewind speed estimate error at each met site. The largest error wasmeasured at Met 6 with an error of 1.61%. Five of the eleven wind speedestimates showed an error of less than 0.50%, which is within theuncertainty of the measurement devices. The RMS error of the wind speedestimates for this example is very low at 0.90%.

The following outlines the steps involved in the methodology of thepresent invention, which is illustrated in FIG. 14. To begin, (Step 1)summary wind data and site data must be input, including: a digitalelevation model; multiple radii of investigation (for example: 4000 m,6000 m, 8000 m, and 10,000 m); and tabular meteorological towerfrequency distributions. A meteorological tower wind speed and winddirection frequency distribution tabular file includes spatialinformation, specifically the locations of the meteorological tower andnumber of direction sectors (usually 12, 16, or 24 sectors). Note thatpreparation of input data is not shown in FIG. 14, but is a prerequisiteto performing the method shown in FIG. 14.

Next, using the inputs above, the (Step 2) the upwind (UW) & downwind(DW) terrain exposures and the terrain complexity for UW and DW arecalculated for each met tower location for each direction sector foreach radius of investigation.

For each unique pair of met towers, the (Step 3) difference between therespective UW & DW terrain complexity is examined. If the difference isgreater than the maximum predetermined allowable difference, a (Step 4)path of nodes between the two met towers is created using an algorithmwith the following parameters:

-   -   The change in terrain complexity from node to node is within the        maximum predetermined allowable difference between terrain        complexity (UW or DW);    -   Each path of nodes follows the gentlest slope possible between        each unique pair of met towers;    -   The nodes selected are on high ground.

Next, using the digital elevation model, radii of investigation, anddirection sectors, the UW & DW terrain exposures and terrain complexityare calculated for each node for each radius of investigation in eachdirection sector. Using the met tower frequency distributions, defaultmodel coefficients, the UW & DW terrain exposures for each node and mettower, and a predetermined UW critical exposure, the (Step 5) windspeeds are cross-predicted for each node and met tower. If thedifference between the respective terrain complexities is less than thepredetermined maximum allowable difference, no path of nodes isrequired, and the wind speeds are cross-predicted directly between themet towers.

Using a self-learning algorithm, the (Step 6) RMS errors are minimizedby progressively adjusting the model coefficients from their defaultvalues. Once the RMS errors are minimized for each cross-prediction,log-log relationships are developed and (Step 7) site-calibratedcoefficients have been derived.

Next, the target site locations (turbine locations or other locations,such as a grid of nodes) are input into the model. Using the digitalelevation model, radii of investigation, and direction sectors, the(Step 8) UW & DW terrain exposures and terrain complexity are calculatedfor each target site for each radius of investigation in each directionsector.

For each pair of target site/met tower, the (Step 9) difference betweenthe respective terrain complexity is examined. If the difference isgreater than the predetermined maximum allowable difference, a (Step 10)path of nodes between the pair of target site/met tower is created usingan algorithm with the following parameters:

-   -   The change in terrain complexity from node to node is within the        predetermined maximum allowable difference between terrain        complexity (UW or DW);    -   Each path of nodes follows the gentlest slope possible between        each pair of target site/met tower;    -   The nodes selected are on high ground.

If the difference between the respective terrain complexity is less thanthe maximum allowable difference, no path of nodes is required, and(Step 11) wind speeds are estimated at the target site directly from mettower to site using the site calibrated coefficients, UW & DW terrainexposures, and the target site UW & DW terrain exposures.

For site/met tower combinations that require a path of nodes, the (Step11) wind speed estimates are derived using the site-calibrated models,UW & DW terrain exposures from the nodes, and the target site UW & DWterrain exposures. This results in different wind speed estimates foreach target site (one for each radius of investigation and individualdirection sector).

To arrive at the final wind speed estimates at each target site, (Step12) weights are derived for each estimate derived in the Step 11 byusing the site-calibrated models from Step 7, the met tower terraincomplexity from Step 2, and the target site terrain complexity from Step8. The wind speed estimates for each target site at each radii ofinvestigation and each direction sector from Step 11 are then (Step 12)averaged using the derived weights from Step 12 to arrive at the (Step13) final wind speed estimates at each target site.

Using the parameters outlined herein, the present innovation can predictwind speeds at any desired location on a prospective wind farm site tohigh level of accuracy. Validation of the model has been conducted andis detailed in Continuum Wind Flow Model: Introduction to Model Theoryand Case Study Review by Elizabeth Walls, incorporated by reference.Validation includes a Round Robin analysis where one or more met towersare systematically removed from the model, and the wind speeds arepredicted at the excluded sites and compared to measured data. Thevalidation revealed the relatively high accuracy of the model.

While an example of the invention has detailed, for those skilled in theart it will be apparent that applications of the model will vary by sitedepending on the parameters and characteristics of each respectiveproject, however any discrepancy from the provided example may be madewithout departing from the scope of the invention. Therefore, theinvention is not limited to the particular example described andillustrated herein.

What is claimed is:
 1. A method of predicting wind speed at one or moretarget sites comprising the steps of: measuring wind speed and directionat one or more meteorological towers at said one or more meteorologicaltowers; defining the location or locations of said one or more targetsites at which wind speed is to be predicted; calculating exposure,using a computer, at said one or more meteorological towers; calculatingterrain complexity using a computer; selecting a wind flow model basedon said terrain complexity wherein said wind flow model relates changein wind speed to change in exposure; calculating exposure at said one ormore target sites and calculating the difference in exposure betweensaid one or more meteorological towers and said one or more prospectivewind turbine sites; and applying said wind flow model using the windspeed at said one or more meteorological towers and said difference inexposure between said one or more meteorological towers and said one ormore target sites to predict a wind speed for said one or more targetsites.
 2. The method of claim 1 wherein said step of selecting a windflow model based on said terrain complexity comprises creating a log-logrelationship between terrain complexity and model coefficients.
 3. Themethod of claim 2 wherein said model coefficients comprise an uphillcoefficient, a downhill coefficient, and a speed-up coefficient.
 4. Themethod of claim 1 wherein said step of calculating terrain complexitycomprises calculating a grid of exposure and selecting said terraincomplexity as an upper percentile of exposure values for said grid. 5.The method of claim 4 wherein said upper percentile of exposure valuesis a P10 value.
 6. The method of claim 1 wherein said wind flow modelcomprises coefficients for uphill wind flow, downhill wind flow, andspeed-up of wind flow over a hill.
 7. The method of claim 6 where eachof said coefficients is selected based on terrain complexity.
 8. Themethod of claim 1 wherein said step of calculating terrain complexitycomprises calculating terrain complexity at said one or moremeteorological towers and at said one or more target sites and furthercomprising the steps of; calculating a difference in terrain complexitybetween said one or more meteorological towers and said one or moretarget sites; comparing said difference in terrain complexity to apredetermined value; and if said difference in terrain complexityexceeds said predetermined value, creating a path of nodes between saidone or more meteorological towers and said one or more target sites andcalculating a wind speed for each said node in said path of nodes. 9.The method of claim 8 wherein said path of nodes is selected to create apath with gradual changes in terrain complexity and elevation along saidpath.
 10. A method of predicting wind speed at one or more target sitescomprising the steps of: measuring wind speed and direction at two ormore meteorological towers; defining the location or locations of saidone or more target sites at which wind speed is to be predicted;calculating exposure, using a computer, at said two or moremeteorological towers and at said one or more target sites; using adefault wind flow model to predict wind speed at the location of each ofsaid two or more meteorological towers based on the wind speed measuredat a different one of said two or more meteorological towers anddifference in exposure between said two or more meteorological towersand calculating an error in said predicted wind speeds for each of saidtwo or more meteorological towers; altering said default wind flow modelbased on said calculated errors to derive a site-calibrated wind flowmodel that results in minimized errors in said predicted wind speeds foreach of said two or more meteorological towers; and applying saidsite-calibrated wind flow model using the measured wind speed at each ofsaid two or more meteorological towers and difference in exposurebetween each of said two or more meteorological towers and said one ormore target sites to predict a wind speed for said one or more targetsites.
 11. The method of claim 10 wherein said wind flow model comprisescoefficients relating change in wind speed to change in exposure. 12.The method of claim 11 wherein said wind flow model comprises differentcoefficients for uphill wind flow, downhill wind flow, and wind flowspeed-up over a hill.
 13. The method of claim 11 wherein said wind flowmodel comprises coefficients relating change in wind speed to change inupwind exposure and to downwind exposure.
 14. The method of claim 13wherein said wind flow model comprises different coefficients for uphillwind flow, downhill wind flow, and wind flow speed-up over a hill. 15.The method of claim 10 further comprising the steps of; calculatingterrain complexity at said two or more meteorological towers and at saidone or more target sites; calculating a difference in terrain complexitybetween said two or more meteorological towers and each other andbetween said two or more meteorological towers and said one or moretarget sites; comparing said differences in terrain complexity to apredetermined value; and if each said differences in terrain complexityexceed said predetermined value, creating a path of nodes between therespective meteorological towers or between a respective meteorologicaltower and said one or more target sites and calculating a wind speed foreach said node in said path of nodes.
 16. The method of claim 15 whereinsaid path of nodes is selected to create a path with gradual changes interrain complexity and elevation along said path.
 17. A method ofpredicting wind speed at one or more target sites comprising the stepsof: measuring wind speed and direction at one or more meteorologicaltowers; defining the location or locations of said one or more targetsites at which wind speed is to be predicted; calculating exposure,using a computer, at said one or more meteorological towers; selecting awind flow model that relates wind speed to exposure and wherein saidwind flow model comprises different sets of coefficients for uphillflow, downhill flow, and wind flow speed-up over a hill; calculatingexposure at said one or more target sites; determining if wind flow isuphill, downhill, or over a hill selecting a set of model coefficientsbased on said wind flow; and applying said wind flow model using themeasured wind speed at said one or more meteorological towers andexposure at said one or more target sites to predict a wind speed forsaid one or more target sites.
 18. The method of claim 17 furthercomprising the step of calculating terrain complexity and wherein saidstep of selecting a wind flow model comprises making said selectionbased on said terrain complexity.
 19. The method of claim 18 whereinsaid step of selecting a wind flow model based on terrain complexitycomprises evaluating a log-log relationship between model coefficientsand terrain complexity.
 20. The method of claim 18 wherein said terraincomplexity is calculated by creating a grid of terrain exposure andselecting exposure values within a predetermined upper percentile withinsaid grid to represent terrain complexity.
 21. The method of claim 20wherein said predetermined upper percentile is a P10 percentile.
 22. Themethod of claim 18 wherein said terrain complexity is compared to apredetermined threshold and if said terrain complexity exceeds saidthreshold then creating a path of nodes between said one or moremeteorological towers and said one or more target sites and calculatingwind speed for each node point along said path.
 23. The method of claim22 wherein said path of nodes is selected to create a path with gradualchanges in terrain complexity and elevation along said path.